KR101042236B1 - Connection-assured communication of wireless sensors - Google Patents

Abstract

PURPOSE: A connection-assured communication of wireless sensors is provided to secure connection time and maximize the communication amount per hour. CONSTITUTION: A number of communication available packets of a start node searched edge is changed by the predetermined value(S140). A maximum algorithm is applied about residual graph(S150). All available routes between receiving nodes and source nodes are searched. The residual graph is created by removing the searched route. The transmitting node and an unused edge which connects the receiving nodes repeat the previous process until the unused edge does not exist. The total traffic paths between the transmitting nodes and the receiving nodes are calculated(S160).

Description

CONNECTION-ASSURED COMMUNICATION OF WIRELESS SENSORS}

The present invention relates to a communication method of a wireless sensor, and more particularly, in a wireless sensor network operated by a battery, a connection for maximizing the amount of communication transmitted per unit time while ensuring a connection time to prevent communication failure caused by battery exhaustion. A method of guaranteeing time-sensitive wireless sensor communication.

Wireless sensor networks are widely used in military surveillance devices, disaster prediction devices, and other environmental monitoring devices. Such wireless sensor networks are generally powered by batteries, which do not necessarily guarantee that the system will operate until the required time simply by reducing energy consumption without degrading operating performance. If an essential node in an ad-hoc network becomes inoperative due to lack of energy, the connectivity of the network will be destroyed because there is no other route.

Meanwhile, as the related art, various routing methods have been proposed to maximize the remaining communication time of the wireless sensor network while satisfying the communication performance required under limited battery conditions. However, these routing schemes do not guarantee the communication connectivity for a given time because they cannot predict the remaining communication time of the network and merely find a routing path that can minimize the reduction of the remaining communication time of the network. Maximizing the remaining communication time of the network while maintaining the performance level can cause unpredictable communication failure due to the consumption of battery energy. On the other hand, a method of maximizing operation performance while ensuring the remaining operation time in a single system with a single battery is proposed, but this method is not applicable to a system composed of a plurality of wireless sensor nodes having a plurality of batteries. In addition, as a method of maximizing the remaining communication time of the wireless sensors, a method of maximizing the remaining communication time while satisfying a given communication amount per unit time has been proposed, but this also cannot guarantee the remaining communication time because it cannot calculate the remaining communication time. There is a limit.

The present invention is to solve the above problems, guaranteeing the connection time to maximize the amount of communication transmitted per unit time while ensuring the connection time to prevent communication failure caused by battery exhaustion in the wireless sensor network operated by the battery An object of the present invention is to provide a wireless sensor communication method.

In order to solve the above problems, the present invention provides a wireless sensor communication method for transmitting a packet while ensuring a connection time in a wireless sensor network consisting of a plurality of wireless sensors, each of the wireless sensors of the wireless sensor network The node, the path based on the communication distance (r ¹ , r ² , ..., r ^k ) between the nodes that can be connected to each other is r ^x edge, and the number of communication packets can be increased for each node. Forming a node capacity graph with a city; Converting the formed node capacity graph into an edge capacity graph; Generating a graph consisting of only r ¹ edges from the edge capacity graph; A fourth step of applying the maximum flow algorithm to the graph generated in the third step to find all possible paths between the transmitting node and the receiving node and removing the found paths to generate a residual graph; For the remaining graph, the unused r ^x edges connecting between the transmitting node and the receiving nodes are found in the order of communication distance (r ² , r ³ , ..., r ^k ), and the start of the found r ^x edge a fifth step of changing in accordance with the number of the communicable nodes in packets of p ^x values, where, p ^x is being energy consumption when transmitting a packet in a communication distance r ^x -; A sixth step of applying the maximum flow algorithm to the residual graph again to find all possible paths between the transmitting node and the receiving nodes, and removing the found paths to generate the residual graph again; And a seventh step of calculating the total path communication volume between the found transmitting node and the receiving node after repeating the fifth and sixth steps until there are no more unused edges connecting the transmitting node and the receiving node. It provides a connection time guaranteed wireless sensor communication method comprising the step.

Here, after the seventh step, the method may further include calculating the number of transmission packets per unit time by dividing the total path communication amount by a given communication time T.

In addition, in the first step, the communication distances r ¹ , r ² ,..., R ^k are r ¹ <r ² <... <r ^k , and the energy consumption rate when transmitting packets at the communication distance r ^x . P ^x may be p ¹ <p ² <p ³ ..... <p ^k .

In addition, when the path found in the fourth step is removed, the transmitting node and the receiving node may be disconnected.

In addition, in the fifth step, the changed number of communicable packets C _f is changed by C _f / α (where α is a normalization constant of the energy consumption rate of r ^x with respect to the energy consumption rate of r ^{x −1} ). Can be.

According to the present invention, it is possible to provide a connection time guaranteed wireless sensor communication method for maximizing the amount of communication transmitted per unit time while ensuring a connection time to prevent communication failure caused by battery exhaustion in a wireless sensor network operated by a battery. .

1 is a graph model for a sensor network with V _s = {n ₁ , n ₂ }, V _i = {n ₃ , n ₄ , n ₅ , n ₆ , n ₇ }, n _t = n _s .
2 is a flowchart illustrating an embodiment of a wireless sensor communication method according to the present invention.
3 shows a conventional maximum flow algorithm.
4 is a diagram illustrating a rule for converting a node capacitive non-directional graph into an edge capacitive directional graph.
5 is a diagram illustrating an example of a flow change.
6 illustrates a flow update algorithm.
7 is a diagram illustrating an example of converting a node capacity non-directional graph into an edge capacity directional graph.
8 is a view showing an example of operation by the method according to the present invention.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

The present invention provides a many-to-one communication method capable of maximizing communication performance (minimizing communication performance reduction) while ensuring a given remaining communication time in a wireless sensor network environment operated by a plurality of batteries. To do this, first, the wireless sensor network to which the present invention is applied will be defined as follows. The wireless sensor network to which the present invention is applied is configured by N sensor nodes, and each sensor node has the following properties.

Most sensor nodes are clustered in a two-dimensional plane to form a sensor network, and all sensor nodes are wireless and fixed.

2. The energy capacity of the sensor node cannot be charged.

3. The location of the sensor nodes and the amount of remaining battery energy are known.

4. All sensor nodes have k transmission ranges (r ¹ , r ² , ......, r ^k ), select any one of them, and short distance It consumes less energy than the communication distance (energy consumption rate according to the communication distance: p ¹ <p ² <p ³ ..... <p ^k ).

5. One base node exists and is called the terminal node.

6. Subsets of sensor nodes are called sender nodes, which collect environmental information and periodically generate data packets. Other sensor nodes are called intermediate nodes and they relay data packets through neighboring nodes to terminal nodes.

A network of wireless sensors under these conditions is modeled with an undirected graph G = {V, E}, where V is a set of nodes and E is a set of undirected edges. Can be. Each node n represents a wireless sensor (V = {n ₁ , n ₂ , n ₃ ,..., N _N }). The terminal node is represented by n _t , the set of transmitting nodes is represented by V _s , and the set of intermediate nodes is represented by V _i . Then V = V _s ∪V _i ∪ {n _t }. Each edge e represents a viable communication link between a pair of sensor nodes. If the distance between nodes n _u and n _v is not greater than the transmission range r ¹ , the non-directional edge e ¹ _{{u, v}} (or e ¹ _{{v, u}} ) is equal to nodes n _u and n _v It is formed between. If the distance between nodes n _u and n _v is greater than the transmission range r ^{1 and} less than r ² , the non-directional edge e ² _{{u, v}} (or e ² _{{v, u}} ) is equal to node n _u . is formed between n _v . edges formed for r ¹ r ¹ is called an edge formed on the edge r ² r ² is referred to as an edge. 1 is a graph model for a sensor network with V _s = {n ₁ , n ₂ }, V _i = {n ₃ , n ₄ , n ₅ , n ₆ , n ₇ }, n _t = n _s . The r ¹ edge is shown by the solid line and the r ² edge is shown by the dotted line.

The connection time, which must be maintained without depletion of energy, is represented by T. The maximum capacity of the transmittable packet for the communication distance r ¹ may be calculated as follows.

Here, a _u is the remaining battery amount of the node n _u , b _u is the basic energy consumption of n _u , p ¹ is the energy consumption rate when transmitting the packet at the communication distance r ¹ .

Similarly, the maximum capacity of the transmittable packet for the communication distance r ² is as follows.

Here, p ² is an energy consumption rate when transmitting a packet at a communication distance r ² .

then,

). 도 1의 예에서 각 노드 n_u 내부의 숫자는 통신 거리 r¹에 대한 전송 가능 패킷의 최대 커패시티(capacity) C¹ _u를 나타낸다. 이와 같은 도 1의 그래프는 노드 커패시티 그래프(node capacity graph)이다.Here, α is a normalization constant (normalized constant) of the power consumption of the communication distance r ² for the communication distance r ¹ (

). In the example of FIG. 1, the number inside each node n _u indicates the maximum capacity C ¹ _u of the transmittable packet for the communication distance r ¹ . Such a graph of FIG. 1 is a node capacity graph.

On the other hand, the terminal node (terminal node) has previously obtained the remaining energy and position of each node. The terminal node calculates a routing path from the sender node to itself and calculates the transmission rate of each routing path. The calculated routing path and rate are broadcast to all transmitting nodes. In this case, it is assumed that each node reserves some of the remaining energy to transmit the collection information of the neighboring transmitting node and the information of the broadcast terminal node.

2 is a flowchart illustrating an embodiment of a wireless sensor communication method according to the present invention for transmitting a packet while guaranteeing connection time in a wireless sensor network formed under such a precondition. 2 is a flowchart illustrating a wireless sensor communication method for transmitting a packet while guaranteeing a connection time in a wireless sensor network including a plurality of wireless sensors configured according to the conditions described above.

2, first, in each of the wireless sensors in a wireless sensor network node (node), the communicable distance between nodes connected to each other is possible, and ^{(r 1, r 2, ...} , r k) The node based path is set as the r ^x edge and a node capacity graph is formed in which the number of communicable packets is capacity for each node (S100). The node capacity graph of step S100 is configured as shown in FIG. 1 as described above, for example. In FIG. 1, the case of two communication distances r ¹ and r ² , but there may be k communication distances, is referred to as an r ^x edge as described above with an edge based on each of them as an edge.

Next, the formed node capacity graph is converted into an edge capacity graph (S110). This is to apply the maximum flow algorithm used in the step (S120) described later because the maximum flow algorithm is designed to operate on the edge capacity graph. Details of this conversion are described below with respect to the maximum flow algorithm.

Next, a graph consisting of only r ¹ edges is generated from the converted edge capacity graph (S120), and a maximum flow algorithm is applied to the graph generated in the step S120 to enable a potential between the transmitting node and the receiving nodes. After finding all paths and removing the found paths, a residual graph is generated (S130), and an unused r ^x edge which connects between the transmitting node and the receiving nodes with respect to the residual graph has a communication distance (r ² , r ³ , ..., r ^k ) in order, and change the number of communicable packets of the start node of the found r ^x edge according to the value of p ^x (S140). Then, the maximum flow algorithm is applied to the residual graph again to find all possible paths between the transmitting node and the receiving nodes, and removes the found paths to generate the residual graph again (S150). After repeating steps S140 and S150 until there are no more unused edges connecting the transmitting node and the receiving node, the total path communication amount between the found transmitting node and the receiving node is calculated ( S160). Once the total path traffic is calculated, it is divided by the given communication time (T), and the number of packets transmitted per unit time is transmitted. According to the number of packets transmitted per unit time, the transmitting node transmits packets to the terminal node to guarantee connection time between wireless sensor nodes. It is possible to maximize the amount of communication transmitted per unit time.

Concepts, terms and specific details used in the steps S110 to S160 will be described in detail below.

First, in the step S110, the reason for converting the node capacity graph into the edge capacity graph is that the maximum flow algorithm used in the present invention as in the steps S130 and S150 is the edge capacity. This is because it is designed to operate on a graph. First, the conventional maximum flow algorithm will be described.

First, see definition 1.

Definition 1.

For a given directional graph G = (V, E) (source node is n _s , terminal node is n _t , and capacity c _{u , v} ≥0 for each directional edge e _{u , v} )), f: E → R Is the flow of the edge of G satisfying

0 ≤f (u, v) ≤C _{u , v} ∀n _u , n _v ∈ V (1)

(2)

(2)

Equation (1), known as a capacity constraint, indicates that the resource used by the flow at an edge cannot exceed the capacity of that edge. Equation (2), known as flow conservation property, is the sum of all flows entering a node (negative flow) is the sum of all flows leaving that node (positive flow). It is equal to. The value of the flow is the total flow leaving n _s and is equal to the total flow entering n _t . The f value is defined as follows.

For the given graph G as above, the maximum flow algorithm finds the maximum flow f (the flow whose value is maximum) from the source node n _s to the terminal node n _t . In the implementation of the maximum-flow algorithm, the concept of residual capacity and residual graph defined as follows is used.

Definition 2.

For flow f at G, the residual capacity of edge e _{u , v} induced by ^f is c ^f _{u, v} = c _{u, v} -f (u, v), and the residual of G derived by f The graph is G ^f = (V, E ^f ) (E ^f = {e _{u, v} : c ^f _{u , v} > 0}).

Figure 3 shows the maximum-flow algorithm proposed by Ford and Fulkerson. This algorithm creates an initial empty flow. This algorithm finds the path from n _s to n _t of the remaining graph G ^{f using} a depth-first search or a depth-first search. The newly found path increases the value of the flow. The path not only has a forward edge with positive residual capacity (ie, e _{u , v} ∈ E ^f ), but also a reverse edge with negative residual capacity. (I.e. e _{v , u with} e _{u , v} uE and f (u, v)> 0). In this f-increasing path, the minimum residual capacity value of the forward edge and the current flow of the reverse edge result in an increase in flow that does not exceed the residual edge capacity. The use of a reverse edge on a new path means that the new path replaces part of the flow with the existing path on the reverse edge. This algorithm searches for new f-growth paths in the residual graph until there are no more f-growth paths.

If there are no more f-increasing paths from n _s to n _t , the searched flow f is proved to be maximum. In this proof, the concepts of cut and capacity of cut defined below are used.

Definition 3.

A cut of G is a set of directional edges that connect a complementary subset containing n _t from a subset of nodes containing n _s . Formula is, K = {e _{u , v} : n _u ∈V'and n _v ∈VV '}, where n _s ∈V' and n _t ∈VV '. The capacity of the cut K is the sum of the capacities of the edges of K (∑ _{eu , v∈K} C _{u , v} ). In the graph G where ∑ _{ew , z∈K´} c _{w , z} <∑e _u, v∈ _K c _{u , v} and there is no cut K´, the cut of G is called the minimum cut. .

The following property 1 indicates that there is no f-increasing path from n _s to n _t when the edge of cut K is removed. For each e _{u , v} of cut K, when f (u, v) = c _{u , v} , the ^f -increasing path because min {c ^f _{u , v} } = 0 for all paths from n _s to n _t Does not exist. The following property 2 indicates that there is another f-increasing path in the residual graph when the flow searched is less than the capacity of the minimum cut.

Properties 1.

The value of flow is not greater than the capacity of all cuts.

Property 2.

The maximum value of the flow is equal to the capacity of the minimum cut.

Since this conventional maximum flow algorithm is designed to operate on an edge-capacity direced graph, it is necessary to convert a node-capacity undirected graph into an edge-capacity directional graph. See step S110 of FIG. 2). 4 is a diagram illustrating a rule for converting a node capacitive non-directional graph into an edge capacitive directional graph.

First, each node n _x of the node capacity graph is divided into two sub-nodes of the edge capacity graph, that is, a receive-oriented sub-node n ^r _x and a send-oriented sub. Convert to node n ^s _x . These two sub-nodes are connected with separate directional edges e _{x , x} in the direction of n ^r _x to n ^s _x . This edge, called the internal edge, e _{x , x} has an edge capacitance with c _{x , x} = C _x , where C _x comes from the node capacitance graph. Each non-directional edge e _{x , y} = e _{y , x} between n _x and n _y in the node capacity graph shows two separate directional edges e _{x , y} and e in the edge capacity graph as shown in FIG. 4. is converted to _{y and x} . These two edges e _{x , y} and e _{y , x} are called external edges and have infinite edge capacity.

For each x, y the initial flow is 0, i.e., f _{x , x} = 0, f _{x , y} = 0, f _{y , x} = 0. In the above-mentioned maximum-flow algorithm, the new f-increasing path is shown in FIG. 5, when there is a positive flow of the existing path on the reverse (reverse edge), the flow is replaced by the existing path. Replace with. In the example of FIG. 5, the existing path transmits 5 flows through an edge sequence e _{w , y} , e _{y , y} , e _{y , x} , e _{x , x} , e _{x , v} . The new path transmits seven flows through the edge sequences e _{u , x} , e _{x , x} , e _{x , y} , e _{y, y} , e _{y , z} as shown on the left side of FIG. 5. This new route as shown on the right side of Figure 5 are identical with those in the case, replace the flow of the node n 5 _x 5, and then the flow of the node n _y following the existing path.

If a new f-increasing path is found in the transformed edge capacity directional graph, the algorithm shown in FIG. 6 updates the residual capacity and the flow value of the edge. The new path replaces a portion of the flow with the existing path when the existing path transmits the flow through the reverse edge e _{y , x} of the edge e _{x , y} of the new path.

7 is a diagram illustrating an example of converting a node capacity non-directional graph into an edge capacity directional graph. Since the maximum flow algorithm is designed to operate on a single source node and a single terminal node, a virtual source node is added to connect to a plurality of transmitting nodes having an infinite capacity edge. The node capacity non-directional graph before the transformation is semantically equivalent to the edge capacity directional graph after the transformation. For a given node capacity non-directional graph, an edge capacity directional graph with virtual source and terminal nodes can be generated correspondingly.

In the following description, for simplicity, it should be noted that only the node capacity non-directional graph is shown, not the edge capacity directional graph.

On the other hand, when the graph G consists of only the r ¹ edge, the maximum number of packets that can be transmitted from the transmitting node to the terminal node is the same as the flow value of the path searched by the existing maximum flow algorithm. However, when the graph G includes r ² edges, the maximum flow algorithm cannot determine the maximum number of packets that can be transmitted since the number of packets that can be transmitted depends on the type of edge used. Therefore, there is a need for a method of improving the maximum flow algorithm for searching for a path having the maximum number of packets that can be transmitted in a graph having two or more types of edges, and the present invention improves the maximum flow algorithm method as shown in FIG. Steps S120 to S160 are performed. As described above, the present invention is applicable to sensor nodes having ^k transmission ranges (r ¹ , r ² , ... r ^k ), but in the following, two communication distances are provided for simplicity. A case having (r ¹ , r ² ) will be described by way of example.

First of all, the key to the maximum flow algorithm improved by the present invention is to find the f-increasing path as described in the maximum flow algorithm described above using as many r ¹ edges as possible. The r ² edge is used to find additional f-increment paths only if the f-increment path consisting of only r ¹ edges no longer exists. The improvement algorithm seen in the first step generates a graph using only r ¹ edges (except r ² edges) from a given graph (corresponding to step S120 of FIG. 2). In the second step, we find all the f-increment paths in the graph consisting of only r ¹ edges. Then there is no remaining path from n _s to n _t in the residual graph (corresponding to step S130 of FIG. 2). In the third step, find the r ² edges that form the path from n _s to n _t in the residual graph. The found r ² edges are added to the rest of the graph. The remaining capacity of the start node of each added r2 edge is replaced with the remaining capacity when using one r ² edge (corresponding to step S140 of FIG. 2). That is, C _f of the start node of each added r ² edge is replaced by C _f / α (α is the normalization constant of the energy consumption rate of r ² with respect to the energy consumption rate of r ¹ ). In the fourth step, the third and fourth steps are repeated until there are no longer unused r ² edges forming a new path from n _s to n _t in the remaining residual graph (steps S150 and S160 of FIG. 2). )).

8 is a view showing an example of operation by the method according to the present invention. The example of FIG. 8 is for the case where it has two communication distances r1 and r2 for convenience of description.

First, the graph of FIG. 8A is formed through the steps S100 and S100 of FIG. 2. However, as described above, since the edge capacity graph corresponding to the node capacity graph exists, it should be noted that in FIG. 8, only the node capacity graph is represented to simplify the description. In the graph of FIG. 8A, n _s = n _o , n _t = n ₁₀ , α = 2.

FIG. 8B illustrates a graph composed of only the r ¹ edges generated in the first step (corresponding to step S120 of FIG. 2). Referring to FIG. 8B, it can be seen that the r2 edge is a graph composed of only the r1 edge in the removed state, and the f-increasing path found in the second step is indicated by a gray arrow.

FIG. 8C illustrates a residual graph obtained after the second step (corresponding to the residual graph after performing step S130 of FIG. 2), and there is no path from n ₀ to n ₁₀ . Nodes with zero residual capacity are not shown here for convenience of description.

FIG. 8D shows the results of the third and fourth steps (corresponding to step S140 and step S150 of FIG. 2). The r ² edges found in the third step are indicated by gray dotted lines. In Figure 8d, adding the edge e _{1, 5,} and e _3, the _six starting node n ₁ and n ₃ of the C ^f ₁ and C ^f ₃ is seen that each replaced by a C ^f ₁ / α and C ^f ₃ / α, respectively Can be. The f-increasing path found in the fourth step is shown by the gray arrow. The gray number next to the gray arrow indicates the number of packets that can be transmitted.

8E illustrates a residual graph obtained after the fourth step. Figure 8f is as showing a result of repeatedly performing the third and fourth steps, e _6,9 is newly added, and C ^f r ² edge ₆ it can be seen that it is replaced with a C ₆ ^f / α.

8G shows the residual graph obtained after repeating the third and fourth steps, where there is no longer an r ² edge with a new path from n ₀ to n ₁₀ . 8F and 8G correspond to performing step S140 and step S150 of FIG. 2 until there is no unused edge in step S160 of FIG.

FIG. 8H shows the number of transmittable packets in all paths searched by this algorithm, which is indicated by the gray number shown next to the gray arrow in FIG. 8H. If the number of such transportable packets is obtained for the entire path, the total path traffic from the source node to the terminal node can be calculated (step S160 in FIG. 2), and the total path traffic is given to the given communication time T. When divided, the number of transmission packets per unit time can be calculated. Accordingly, it is possible to maximize the number of packets that can be transmitted per unit time while ensuring a connection time in a wireless sensor network composed of a plurality of sensor nodes.

Meanwhile, FIG. 8I shows a minimum cut, whereby the number of packets that can be transmitted is equal to the number of packets that are transmitted on the searched path. The cut consists of n ₁ , n ₄ , n ₃ .

The following pseudo-code is an example of FIG. 8 and is called a maximum-packets algorithm.

Step 1: Create a graph consisting of only r ¹ edges in a given graph.

Step 2: Apply the maximum path algorithm until there is no f-increasing path in the graph created in step 1.

Step 3: Find the r ² edges that form the path from n _s to n _t of the residual graph. It replaces the added edge r ² remaining capacity of each starting node C ^f _u as a residual capacity C ^f _u / α r ² When Using the edge.

Step 4: Apply the maximum flow algorithm to the residual graph with the added r ² edges.

Step 5: Repeat steps 3 and 4 above until there are no longer unused r ² edges forming a path from n _s to n _t of the residual graph.

As described above, this step 1 to step 5 can be applied to a sensor network having k communication distances, and as described in FIG. 2, edges not yet used are r ² , r ³ , ...., r Repeated application of the maximum flow algorithm while changing the remaining traffic of the starting node at the r ^x edges found in order of ^k will not only apply to the two communication distances but also to networks with k communication distances as shown in FIG. It becomes possible.

The present invention described above proposes a method in which a plurality of transmitting nodes can communicate with a terminal node for a given time without a communication obstacle due to energy depletion under a given energy capacity condition. Therefore, according to the present invention, it is possible to provide a method for maximizing packet communication in a wireless sensor network while ensuring connection time for a given communication time.

Claims (5)

In the wireless sensor communication method for transmitting a packet while guaranteeing a connection time in a wireless sensor network consisting of a plurality of wireless sensors,
K wireless communication distances r ¹ , r ² ,..., R ^k between the wireless sensors in the wireless sensor network as nodes, and which can be connected to each other, where k communication distances r ¹ , r ² , ..., r ^k ) is r ¹ <r ² <... <r ^k , and the packet is transmitted at each communication distance (r ¹ , r ² , ..., r ^k ) The energy consumption rates (p ¹ , p ² , p ³ , ...., p ^k ) are r ^x edges, where p ¹ <p ² <p ³ ..... <p ^k ) Forming a node capacity graph for which the number of communicable packets is the capacity for each node;
Converting the formed node capacity graph into an edge capacity graph;
Generating a graph consisting of only r ¹ edges from the edge capacity graph;
A fourth step of applying the maximum flow algorithm to the graph generated in the third step to find all possible paths between the transmitting node and the receiving node and removing the found paths to generate a residual graph;
For the remaining graph, the unused r ^x edges connecting between the transmitting node and the receiving nodes are found in the order of communication distance (r ² , r ³ , ..., r ^k ), and the start of the found r ^x edge a fifth step of changing in accordance with the number of the communicable nodes in packets of p ^x values, where, p ^x is being energy consumption when transmitting a packet in a communication distance r ^x -;
A sixth step of applying the maximum flow algorithm to the residual graph again to find all possible paths between the transmitting node and the receiving nodes, and removing the found paths to generate the residual graph again; And
A seventh step of repeating the fifth and sixth steps until there are no more unused edges connecting the transmitting node and the receiving node, and then calculating the total path communication volume between the found transmitting node and the receiving node;
Including,
And after the seventh step, calculating the number of transmission packets per unit time by dividing the total path communication amount by a given communication time (T).
delete delete The method of claim 1,
If the path found in step 4 is removed, the transmitting node and the receiving node are disconnected.
The method of claim 1,
In the fifth step, the changed number of communicable packets C _f is changed by C _f / α, where α is a normalization constant of the energy consumption rate of r ^x with respect to the energy consumption rate of r ^{x −1} . Connection time guarantee wireless sensor communication method.

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